Within Life Cycle Impact Assessment (LCIA), characterization factors (CFs) for chemically- mediated human health (HH) impacts are a combination of exposure and toxicity. "description": "Qualified estimate (e.g.ABSTRACT. "description": "Non-verified data partly based on qualified estimates", "description": "Data on related processes on laboratory scale or from different technology", "description": "Data from processes and materials under study but from different technology", identical technology) but from different enterprises", "description": "Data from processes and materials under study (i.e. "description": "Data on related processes or materials", "description": "Data from enterprises, processes and materials under study", "name": "Further technological correlation", "description": "Data from area with slightly similar production conditions", "description": "Data from unknown or distinctly different area (North America instead of Middle East, OECD-Europe instead of Russia)", "description": "Data from area under study", "description": "Data from area with similar production conditions", "description": "Average data from larger area in which the area under study is included", "description": "Representative data from \u003e 50% of the sites relevant for the market considered, over an adequate period to even out normal fluctuations", "description": "Representativeness unknown or data from a small number of sites and from shorter periods",
"description": "Representative data from only one site relevant for the market considered or some sites but from shorter periods", "description": "Representative data from only some sites (\u003c\u003c 50%) relevant for the market considered or \u003e 50% of sites but from shorter periods", "description": "Representative data from all sites relevant for the market considered, over and adequate period to even out normal fluctuations", "description": "Less than 6 years of difference to the time period of the data set", "description": "Less than 3 years of difference to the time period of the data set", "description": "Age of data unknown or more than 15 years of difference to the time period of the data set", "description": "Less than 10 years of difference to the time period of the data set",
"description": "Less than 15 years of difference to the time period of the data set", The timestamp when the entity was changed the last time.
SINGLE SCORE OPENLCA PATCH
The identifier is typically just the combination of the library name and version.Ī version number in format where the MINOR and PATCH fields are optional and the fields may have leading zeros (so 01.00.00 is the same as 1.0.0 or 1). If this entity is part of a library, this field contains the identifier of that library. A tag is just a string which should not contain commas (and other special characters). Properties from CategorizedEntity: categoryĪ list of optional tags. However, internally the numeric values are used in the data model and calculations. These labels are then displayed instead of 1, 2, 3. for the DQ scores can be assigned by the user. 4) by applying an weighted average and rounding.įinally, custom labels like A, B, C. 5) in a process $q$ could be aggregated to (2 3 2 n.a. 2) in a process $p$ and a contribution of 1.5 kg and a data quality entry of (2 3 1 n.a. For example, the data quality entry of a flow $f$ with a contribution of 0.5 kg and a data quality entry of (3 2 4 n.a. In calculations, these data quality entries can be aggregated in different ways.
2) which means the data quality score for the first indicator is 3, for the second 2 etc. In openLCA, the data quality entry $d$ of a process or exchange is stored as a string like (3 2 4 n.a. The possible values of the data quality scores are defined as a linear order $1 \dots n$. Such a system can then be used to assess the data quality of processes and exchanges by tagging them with an instance of the system $D$ where $D$ is a $m * n$ matrix with an entry $d_$ containing the value of the data quality score $j$ for indicator $i$.Īs each indicator in $D$ can only have a single score value, $D$ can be stored in a vector $d$ where $d_i$ contains the data quality score for indicator $i$. A data quality system (DQS) in openLCA describes a pedigree matrix of $m$ data quality indicators (DQIs) and $n$ data quality scores (DQ scores).